Zomato Rating Prediction¶

Importing The Required Libraries & Packages

In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
import seaborn as sns
import plotly.graph_objs as go
import plotly.offline as py
import matplotlib.ticker as mtick
plt.style.use('fivethirtyeight')
from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import ExtraTreesRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import RandomizedSearchCV
from sklearn.model_selection import train_test_split
import warnings
warnings.filterwarnings('ignore')

Changing The Default Working Directory & Path

In [2]:
os.getcwd()
Out[2]:
'C:\\Users\\chand'
In [3]:
os.chdir('D:\\bala\\study things\\data science\\Batch 74 Day 37\\Batch 74 Day 37\\Project 11 Flask Project and Deployment\\')

Reading The Dataset Using Pandas Command

In [4]:
data = pd.read_csv('zomato.csv')

Data Cleaning¶

Checking the Null values of all the Columns in the dataset

In [5]:
data.isnull().sum()
Out[5]:
url                                0
address                            0
name                               0
online_order                       0
book_table                         0
rate                            7775
votes                              0
phone                           1208
location                          21
rest_type                        227
dish_liked                     28078
cuisines                          45
approx_cost(for two people)      346
reviews_list                       0
menu_item                          0
listed_in(type)                    0
listed_in(city)                    0
dtype: int64

Dropping the unwanted column in the dataset

In [6]:
df = data.drop(['url','phone'],axis=1)

Checking the dataset whether its is having a duplicate values or not

In [7]:
print(df.duplicated().sum())

43

Dropping all the duplicate value from the dataset and checking the duplicate value after dropping it.

In [8]:
df.drop_duplicates(inplace=True)
display(df.duplicated().sum())

0

Dropping all the null value from the dataset and Checking the Null values of all the Columns after dropping it.

In [9]:
df.dropna(how='any',inplace=True)
display(df.isnull().sum())

address                        0
name                           0
online_order                   0
book_table                     0
rate                           0
votes                          0
location                       0
rest_type                      0
dish_liked                     0
cuisines                       0
approx_cost(for two people)    0
reviews_list                   0
menu_item                      0
listed_in(type)                0
listed_in(city)                0
dtype: int64

Getting all the column from the dataset

In [10]:
display(df.columns)

Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'location', 'rest_type', 'dish_liked', 'cuisines',
       'approx_cost(for two people)', 'reviews_list', 'menu_item',
       'listed_in(type)', 'listed_in(city)'],
      dtype='object')

Renaming the columns of the dataset for making it simple

In [11]:
df = df.rename(columns={'approx_cost(for two people)':'cost','listed_in(type)':'type','listed_in(city)':'city'})

Checking the column name from the dataset after renaming it

In [12]:
df.columns
Out[12]:
Index(['address', 'name', 'online_order', 'book_table', 'rate', 'votes',
       'location', 'rest_type', 'dish_liked', 'cuisines', 'cost',
       'reviews_list', 'menu_item', 'type', 'city'],
      dtype='object')

Getting all the unique value of the cost column from the dataset

In [13]:
display(df['cost'].unique())

array(['800', '300', '600', '700', '550', '500', '450', '650', '400',
       '750', '200', '850', '1,200', '150', '350', '250', '1,500',
       '1,300', '1,000', '100', '900', '1,100', '1,600', '950', '230',
       '1,700', '1,400', '1,350', '2,200', '2,000', '1,800', '1,900',
       '180', '330', '2,500', '2,100', '3,000', '2,800', '3,400', '40',
       '1,250', '3,500', '4,000', '2,400', '1,450', '3,200', '6,000',
       '1,050', '4,100', '2,300', '120', '2,600', '5,000', '3,700',
       '1,650', '2,700', '4,500'], dtype=object)

Updating the cost column with the appropriate form of numerics

In [14]:
df['cost']=df['cost'].apply(lambda x:x.replace(',',''))

Getting all the unique value of the cost column from the dataset after changing it to numeric form

In [15]:
display(df['cost'].unique())

array(['800', '300', '600', '700', '550', '500', '450', '650', '400',
       '750', '200', '850', '1200', '150', '350', '250', '1500', '1300',
       '1000', '100', '900', '1100', '1600', '950', '230', '1700', '1400',
       '1350', '2200', '2000', '1800', '1900', '180', '330', '2500',
       '2100', '3000', '2800', '3400', '40', '1250', '3500', '4000',
       '2400', '1450', '3200', '6000', '1050', '4100', '2300', '120',
       '2600', '5000', '3700', '1650', '2700', '4500'], dtype=object)

Changing the data type of the cost column from "Object" to "Float"

In [16]:
df['cost']=df['cost'].astype('float')

Checking the value counts for cost columns from the dataset

In [17]:
df.cost.value_counts()
Out[17]:
400.0     2485
500.0     2323
600.0     2175
300.0     1694
800.0     1664
1000.0    1242
700.0     1207
1200.0     908
200.0      873
1500.0     866
450.0      721
250.0      701
750.0      651
650.0      645
350.0      553
900.0      528
1300.0     496
1100.0     466
1400.0     453
550.0      440
150.0      371
2000.0     298
1600.0     241
1700.0     233
1800.0     193
3000.0     159
2500.0     143
850.0      122
100.0      120
2100.0      63
1900.0      58
950.0       47
2200.0      39
2800.0      31
3500.0      25
4000.0      24
2400.0      22
1350.0      18
180.0       17
3400.0      13
230.0       10
1250.0       9
40.0         8
1650.0       6
1450.0       5
2600.0       4
1050.0       4
4100.0       4
330.0        4
2700.0       2
3200.0       2
120.0        2
6000.0       2
4500.0       2
5000.0       1
3700.0       1
2300.0       1
Name: cost, dtype: int64

Updating the rate column without having "NEW" in it for the future processing

In [18]:
df = df.loc[df.rate !='NEW']

Getting all the unique value of the rate column from the dataset

In [19]:
display(df['rate'].unique())

array(['4.1/5', '3.8/5', '3.7/5', '4.6/5', '4.0/5', '4.2/5', '3.9/5',
       '3.0/5', '3.6/5', '2.8/5', '4.4/5', '3.1/5', '4.3/5', '2.6/5',
       '3.3/5', '3.5/5', '3.8 /5', '3.2/5', '4.5/5', '2.5/5', '2.9/5',
       '3.4/5', '2.7/5', '4.7/5', '2.4/5', '2.2/5', '2.3/5', '4.8/5',
       '3.9 /5', '4.2 /5', '4.0 /5', '4.1 /5', '2.9 /5', '2.7 /5',
       '2.5 /5', '2.6 /5', '4.5 /5', '4.3 /5', '3.7 /5', '4.4 /5',
       '4.9/5', '2.1/5', '2.0/5', '1.8/5', '3.4 /5', '3.6 /5', '3.3 /5',
       '4.6 /5', '4.9 /5', '3.2 /5', '3.0 /5', '2.8 /5', '3.5 /5',
       '3.1 /5', '4.8 /5', '2.3 /5', '4.7 /5', '2.4 /5', '2.1 /5',
       '2.2 /5', '2.0 /5', '1.8 /5'], dtype=object)

Updating the rate column as it can be tansformed into proper form of numerics

In [20]:
df['rate']=df['rate'].apply(lambda x:x.replace('/5',''))

Getting all the unique value of the rate column from the dataset after updating it.

In [21]:
display(df['rate'].unique())

array(['4.1', '3.8', '3.7', '4.6', '4.0', '4.2', '3.9', '3.0', '3.6',
       '2.8', '4.4', '3.1', '4.3', '2.6', '3.3', '3.5', '3.8 ', '3.2',
       '4.5', '2.5', '2.9', '3.4', '2.7', '4.7', '2.4', '2.2', '2.3',
       '4.8', '3.9 ', '4.2 ', '4.0 ', '4.1 ', '2.9 ', '2.7 ', '2.5 ',
       '2.6 ', '4.5 ', '4.3 ', '3.7 ', '4.4 ', '4.9', '2.1', '2.0', '1.8',
       '3.4 ', '3.6 ', '3.3 ', '4.6 ', '4.9 ', '3.2 ', '3.0 ', '2.8 ',
       '3.5 ', '3.1 ', '4.8 ', '2.3 ', '4.7 ', '2.4 ', '2.1 ', '2.2 ',
       '2.0 ', '1.8 '], dtype=object)

Visualizations¶

Assigning the Top 20 restaurants to the new variable for the simple visualization

In [22]:
chains = df['name'].value_counts()[:20]

Plotting the bar graph for the most famous restaurants(Top 20 restaurants) and saving the PNG file

In [23]:
plt.figure(figsize=(10,10))
chains=df['name'].value_counts()[:20]
sns.barplot(x=chains,y=chains.index,palette='deep')
plt.title('Most famous restaurants chains in Bengaluru')
plt.xlabel('Number of outlets')
plt.savefig('Most Famous Restaurants in Bengaluru.png')
plt.show()

Plotting the Pie-Chart with table booking value.

In [24]:
x=df['book_table'].value_counts()
colors = ['#800080', '#0000A0']
trace=go.Pie(labels=x.index,values=x,textinfo="value",
            marker=dict(colors=colors, 
                           line=dict(color='#001000', width=2)))
layout=go.Layout(title="Table booking",width=600,height=600)
fig=go.Figure(data=[trace],layout=layout)
py.iplot(fig, filename='pie_chart_subplots')
plt.show()

Plotting the bar Plot with the online delivery using seaborn count plot package and saving the PNG file

In [25]:
sns.countplot(x='online_order',data = df)
fig = plt.gcf()
fig.set_size_inches(10,10)
plt.title('Whether Restaurants deliver online or not')
plt.savefig(' Online delivery of restaurants.png')
plt.show()

Visualizing the data distribution of a rate column against the density distribution using Seaborn Distplot and saving the PNG file

In [26]:
sns.distplot(df['rate'],bins=20)
plt.title('Data distribution of rate column')
plt.savefig('Data distribution of rate column.png')
plt.show()

Getting the Minumim and Maximum Rating of the Restaurants

In [27]:
display(df['rate'].min())
display(df['rate'].max())

'1.8'
'4.9 '

Getting all the unique value of the rate column from the dataset

In [28]:
df.rate.unique()
Out[28]:
array(['4.1', '3.8', '3.7', '4.6', '4.0', '4.2', '3.9', '3.0', '3.6',
       '2.8', '4.4', '3.1', '4.3', '2.6', '3.3', '3.5', '3.8 ', '3.2',
       '4.5', '2.5', '2.9', '3.4', '2.7', '4.7', '2.4', '2.2', '2.3',
       '4.8', '3.9 ', '4.2 ', '4.0 ', '4.1 ', '2.9 ', '2.7 ', '2.5 ',
       '2.6 ', '4.5 ', '4.3 ', '3.7 ', '4.4 ', '4.9', '2.1', '2.0', '1.8',
       '3.4 ', '3.6 ', '3.3 ', '4.6 ', '4.9 ', '3.2 ', '3.0 ', '2.8 ',
       '3.5 ', '3.1 ', '4.8 ', '2.3 ', '4.7 ', '2.4 ', '2.1 ', '2.2 ',
       '2.0 ', '1.8 '], dtype=object)

Changing the data type of the cost column from "Object" to "Float"

In [29]:
df['rate']=df['rate'].astype('float')

Getting all the unique value of the rate column from the dataset after updating the data types

In [30]:
df.rate.unique()
Out[30]:
array([4.1, 3.8, 3.7, 4.6, 4. , 4.2, 3.9, 3. , 3.6, 2.8, 4.4, 3.1, 4.3,
       2.6, 3.3, 3.5, 3.2, 4.5, 2.5, 2.9, 3.4, 2.7, 4.7, 2.4, 2.2, 2.3,
       4.8, 4.9, 2.1, 2. , 1.8])

plotting the Bar Graph using matplotlib package with the rate column grouping it by the unit difference and saving the PNG file

In [31]:
group = [1,2,3,4,5]
plt.hist(df['rate'],group,histtype='bar',rwidth=0.5,color='b')
plt.title('Rating Unit Difference size')
plt.savefig('Rating unit Difference Size.png')
plt.show()

plotting the Bar Graph using matplotlib package with the rate column grouping it by the unit difference and saving the PNG file

In [32]:
group = [1.5,2,2.5,3,3.5,4,4.5,5]
plt.hist(df['rate'],group,histtype='bar',rwidth=0.5,color='b')
plt.title('Rating 0.5 Unit Difference size')
plt.savefig('Rating 0.5 unit Difference Size.png')
plt.show()

Assigning the different variables for the rate column for using Pie Chart to visualize the percentage of restaurants according to ratings.

In [33]:
gr_1to2=((df['rate']>=1) & (df['rate']<2)).sum()
gr_2to3=((df['rate']>=2) & (df['rate']<3)).sum()
gr_3to4=((df['rate']>=3) & (df['rate']<4)).sum()
gr_4to5=(df['rate']>=4).sum()

Plotting the pie chart with assigned value to visualize the percentage of restaurants according to ratings and saving the PNG file.

In [34]:
slices=[gr_1to2,gr_2to3,gr_3to4,gr_4to5]   
labels=['Rating 1 to 2','Rating 2 to 3','Rating 3 to 4','Rating >4']
colors = ['#ff3333','#c2c2d6','#6699ff']
plt.pie(slices,colors=colors, labels=labels, autopct='%1.0f%%', pctdistance=.5, labeldistance=1.2,shadow=True)
fig = plt.gcf()
plt.title("Percentage of Restaurants according to their ratings")
plt.savefig('Percentage of Restaurants according to their ratings.png')
fig.set_size_inches(10,10)
plt.show()

Plotting the bar Plot with the service type using seaborn count plot package and saving the PNG file

In [35]:
sns.countplot(x= 'type', data = df).set_xticklabels(sns.countplot(x= 'type' , data = df).get_xticklabels(), rotation=90, ha="right")
fig = plt.gcf()
fig.set_size_inches(20,12)
plt.title('Type of Service')
plt.savefig('type of Service.png')
plt.show()

Grouping by the cost column from the dataset by their sizes

In [36]:
print (df.groupby('cost').size())

cost
40.0         8
100.0      120
120.0        2
150.0      371
180.0       17
200.0      869
230.0       10
250.0      697
300.0     1688
330.0        4
350.0      553
400.0     2478
450.0      719
500.0     2321
550.0      440
600.0     2168
650.0      644
700.0     1202
750.0      651
800.0     1647
850.0      122
900.0      524
950.0       47
1000.0    1218
1050.0       4
1100.0     450
1200.0     903
1250.0       8
1300.0     496
1350.0      18
1400.0     447
1450.0       5
1500.0     841
1600.0     237
1650.0       6
1700.0     229
1800.0     193
1900.0      58
2000.0     298
2100.0      60
2200.0      39
2300.0       1
2400.0      22
2500.0     143
2600.0       4
2700.0       2
2800.0      31
3000.0     159
3200.0       2
3400.0      13
3500.0      25
3700.0       1
4000.0      24
4100.0       4
4500.0       2
5000.0       1
6000.0       2
dtype: int64

Plotting the box plot with cost value

In [37]:
from plotly.offline import iplot
trace0=go.Box(y=df['cost'],name="accepting online orders",
              marker = dict(
        color = 'rgb(113, 10, 100)',
    ))
data=[trace0]
layout=go.Layout(title="Box plot of approximate cost",width=800,height=800,yaxis=dict(title="Price"))
fig=go.Figure(data=data,layout=layout)
py.iplot(fig)

Visualizing the data distribution of a cost column against the density distribution using Seaborn Distplot and saving the PNG file

In [38]:
plt.figure(figsize=(8,8))
sns.distplot(df['cost'])
plt.title('Data distribution of cost column')
plt.savefig('Data distribution of cost column.png')
plt.show()

By using Regular Expression Package,splitting occurs in disliked columns as multiple values seperated by comma,Extract each dishes and creating a list by appending the each dishes

In [39]:
import re
df.index=range(df.shape[0])
likes=[]
for i in range(df.shape[0]):
    array_split=re.split(',',df['dish_liked'][i])
    for item in array_split:
        likes.append(item)

Displaying the each number of dishes and its counts

In [40]:
display ('Number of dishes', len(likes))
display(likes)

'Number of dishes'
126848
['Pasta',
 ' Lunch Buffet',
 ' Masala Papad',
 ' Paneer Lajawab',
 ' Tomato Shorba',
 ' Dum Biryani',
 ' Sweet Corn Soup',
 'Momos',
 ' Lunch Buffet',
 ' Chocolate Nirvana',
 ' Thai Green Curry',
 ' Paneer Tikka',
 ' Dum Biryani',
 ' Chicken Biryani',
 'Churros',
 ' Cannelloni',
 ' Minestrone Soup',
 ' Hot Chocolate',
 ' Pink Sauce Pasta',
 ' Salsa',
 ' Veg Supreme Pizza',
 'Masala Dosa',
 'Panipuri',
 ' Gol Gappe',
 'Onion Rings',
 ' Pasta',
 ' Kadhai Paneer',
 ' Salads',
 ' Salad',
 ' Roti',
 ' Jeera Rice',
 'Farmhouse Pizza',
 ' Chocolate Banana',
 ' Virgin Mojito',
 ' Pasta',
 ' Paneer Tikka',
 ' Lime Soda',
 ' Prawn Pizza',
 'Pizza',
 ' Mocktails',
 ' Coffee',
 ' Nachos',
 ' Salad',
 ' Pasta',
 ' Sandwiches',
 'Waffles',
 ' Pasta',
 ' Coleslaw Sandwich',
 ' Choco Waffle',
 ' Tacos',
 ' Momos',
 ' Cheese Nachos',
 'Waffles',
 ' Pasta',
 ' Crispy Chicken',
 ' Honey Chilli Chicken',
 ' Sandwich',
 ' Coffee',
 ' Crepe',
 'Mocktails',
 ' Peri Fries',
 ' Lasagne',
 ' Pizza',
 ' Chicken Bbq Wings',
 ' Virgin Mojito',
 ' Nachos',
 'Coffee',
 ' Spaghetti',
 ' Pancakes',
 ' Nachos',
 ' Pasta',
 ' Sandwich',
 ' Garlic Bread',
 'Sandwich',
 ' Omelette',
 ' Ice Tea',
 ' Virgin Mojito',
 ' Hot Chocolate',
 ' Pasta Arrabiata',
 ' Hazelnut Cappuccino',
 'Churros',
 ' Cannelloni',
 ' Minestrone Soup',
 ' Hot Chocolate',
 ' Pink Sauce Pasta',
 ' Salsa',
 ' Veg Supreme Pizza',
 'Garlic Bread',
 ' Burgers',
 ' Sandwiches',
 ' Pizza',
 ' Hot Chocolate Fudge',
 ' Waffles',
 ' Potato Wedges',
 'Momos',
 ' Mushroom Bruschettas',
 ' Jalapeno Cheeseballs',
 ' Pasta',
 ' Red Velvet Shake',
 ' Red Velvet Milkshake',
 ' Maggi',
 'Pasta',
 ' Gelato',
 ' Garlic Bread',
 ' Mojito',
 ' Nachos',
 ' Paneer Pizza',
 ' Mocktails',
 'Cheese Maggi',
 ' Peri Peri Fries',
 ' Pasta Arrabiata',
 'Burgers',
 ' Caramel Milkshake',
 ' Iced Coffee',
 ' Iced Tea',
 'Pizza',
 ' Garlic Bread',
 'Pasta',
 ' Iced Tea',
 'Paratha',
 ' Sandwich',
 ' Chilli Idli',
 ' Noodles',
 ' Sandwiches',
 ' Lime Soda',
 'Pizza',
 ' Fries',
 ' Anjeer Ice Cream',
 ' Sandwiches',
 ' Gelato',
 ' Burgers',
 ' Kesar Badam',
 'Burgers',
 ' Lassi',
 ' Chicken Grill',
 ' Naan',
 ' Momos',
 ' Chicken Burger',
 ' Biryani',
 'Paneer Tikka',
 ' Garlic Bread',
 ' Thin Crust Pizza',
 ' Falafel',
 ' Veggie Pizza',
 'Rolls',
 ' Veggie Wrap',
 ' Chocolate Fantasy',
 ' Rice Bowls',
 ' Chicken Rice',
 ' Paneer Makhanwala',
 ' Chicken Tikka Wrap',
 'Raita',
 ' Gulab Jamun',
 ' Paneer Biryani',
 ' Vegetable Biryani',
 ' Gosht Dum Biryani',
 ' Chicken Biryani',
 ' Murgh Biryani',
 'Noodles',
 ' Momos',
 ' Veg Manchow Soup',
 ' Kung Pao Chicken',
 ' Chicken Lollipop',
 ' Chop Suey',
 ' Dragon Chicken',
 'Chicken Tikka Masala',
 ' Mutton Keema Dosa',
 ' Mutton Biryani',
 ' Raan Biryani',
 ' Brain Fry',
 ' Chicken Grill',
 ' Ghee Rice',
 'Dabeli',
 ' Faluda',
 ' Masala Poori',
 ' Sandwiches',
 ' Tokri Chaat',
 ' Pav Bhaji',
 ' Dahipuri',
 'Burgers',
 ' Fries',
 ' Hot Chocolate',
 'Pane Pizza',
 ' Lava Cake',
 ' Pasta',
 ' Stuffed Garlic Bread',
 ' Chocolava Cake',
 ' Tacos',
 'Farmhouse Pizza',
 ' Chocolate Banana',
 ' Virgin Mojito',
 ' Pasta',
 ' Paneer Tikka',
 ' Lime Soda',
 ' Prawn Pizza',
 'Prompt Service',
 'Biryani',
 'Butter Chicken',
 ' Noodles',
 ' Thali',
 ' Biryani',
 'Momos',
 ' Dragon Chicken',
 ' Hakka Noodles',
 ' Chowmein',
 ' Chop Suey',
 ' Chilli Garlic Noodles',
 ' Garlic Rice',
 'Paratha',
 ' Pasta',
 'Hot Chocolate Fudge',
 ' Vanilla Ice Cream',
 ' Fudge Cake',
 ' Chocolate Mousse',
 'Prawn Biryani',
 ' Dragon Chicken',
 ' Chicken Boneless Biryani',
 ' Afghani Biryani',
 ' Mughlai Biryani',
 ' Paneer Tikka Biryani',
 ' Gongura Biryani',
 'Pasta',
 ' Momos',
 ' Rolls',
 ' Chicken Roll',
 ' Chilli Chicken',
 ' Paneer Roll',
 ' Spaghetti',
 'Salads',
 ' Sandwiches',
 ' Salad',
 ' Thai Rice',
 ' Pasta',
 ' Noodles',
 ' Pizza',
 'Sushi',
 ' Noodles',
 ' Wine',
 ' Khau Suey',
 ' Mocktails',
 ' Yakisoba',
 ' Spring Roll',
 'Pasta',
 ' Pepper Pizza',
 ' Paneer Pizza',
 ' Peppy Paneer',
 ' Nachos',
 ' Supreme Pizza',
 ' Manchow Soup',
 'Sea Food',
 ' Crab Meat',
 ' Crab Soup',
 ' Fish Curry',
 ' Chicken Cafreal',
 ' Butter Naan',
 ' Butter Chicken',
 'Salads',
 ' Panneer Butter Masala',
 ' Rabri',
 ' Gajar Ka Halwa',
 ' Manchow Soup',
 ' Aam Panna',
 ' Tomato Basil Soup',
 'Grape Juice',
 ' Chicken Grill',
 ' Chicken Tikka Roll',
 ' Shawarma Roll',
 ' Chicken Shawarama',
 ' Biryani Combo',
 ' Rolls',
 'Beer',
 ' Wedges',
 ' Pizza',
 ' Nachos',
 ' Salads',
 ' Papaya Salad',
 ' Buffalo Wings',
 'Vegetable Biryani',
 ' Ginger Chicken',
 ' Chicken Biryani',
 ' Donne Biryani',
 ' Chicken Kebab',
 ' Egg Bhurji',
 ' Dum Biryani',
 'Rolls',
 ' Momos',
 ' Fries',
 ' Veggie Roll',
 ' Chicken Kebab Roll',
 ' Paneer Roll',
 ' Cheese Roll',
 'Benne Dosa',
 ' Mirchi Bajji',
 ' Filter Coffee',
 ' Poha',
 ' Tea',
 ' Butter Dosa',
 ' Crispy Dosa',
 'Masala Dosa',
 'Berryblast',
 ' Waffles',
 ' Cheesecake',
 ' Salted Caramel',
 'Chicken Boneless Biryani',
 ' Hyderabadi Biryani',
 ' Paneer Biryani',
 ' Tandoori Chicken',
 ' Vegetable Biryani',
 ' Chicken Kebab',
 ' Raita',
 'Biryani',
 ' Chicken Guntur',
 ' Thali',
 ' Buttermilk',
 ' Manchurian',
 'Chilli Chicken',
 'Panipuri',
 ' Chaat',
 ' Noodles',
 ' Dahi Bhalle',
 'Hot Chocolate Fudge',
 'Rooftop Ambience',
 'Coffee',
 ' Tea',
 ' Kesari Bath',
 ' Open Dosa',
 ' Vada',
 ' Idli',
 ' Bread Dosa',
 'Cup Cake',
 ' Chocolate Cake',
 'Masala Dosa',
 ' Tirupathi Pongal',
 ' Filter Coffee',
 ' Vada',
 ' Kharabath',
 ' Kali Dosa',
 ' Rava Idli',
 'Panner Tikka',
 ' Kulfi',
 ' Coffee',
 ' Pav Bhaji',
 ' Carrot Halwa',
 ' Fried Rice',
 ' Set Dosa',
 'Paneer Tikka',
 ' Dal Kichadi',
 ' French Fries',
 'Filter Coffee',
 ' Masala Dosa',
 ' Idli',
 ' Vada',
 'Rolls',
 ' Shawarma',
 ' Butter Chicken',
 ' Mutton Biryani',
 ' Lahori Chicken',
 ' Chicken Roll',
 ' Grape Juice',
 'Rolls',
 ' Paratha',
 ' Chicken Biryani',
 'Paratha',
 ' Mughlai Chicken',
 ' Masala Lemonade',
 ' Butter Chicken',
 ' Rajma',
 ' Sandwich',
 ' Salad',
 'Burgers',
 ' French Fries',
 'Rolls',
 ' Ice Cream Roll',
 ' Paan Ice Cream',
 ' Coffee',
 ' Vanilla Ice Cream',
 ' Chocolate Icecreams',
 ' Mississippi Mudpie',
 'Kadhai Paneer',
 ' Ghee Rice',
 ' Chicken Grill',
 ' Shawarma',
 ' Paratha',
 ' Biryani',
 ' Butter Chicken',
 'Chicken Biryani',
 ' Rolls',
 'Roast Pork',
 ' Tai Pai Chicken',
 ' Beer',
 ' Sweet Corn Chicken Soup',
 ' Masala Peanuts',
 ' Chinese Chop Suey',
 ' Chicken Lollipop',
 'Pizza',
 ' Nachos',
 ' Garlic Maggi',
 ' Burgers',
 ' Cheese Maggi',
 ' Bruschetta Bites',
 ' Sandwich',
 'Noodles',
 ' Laksa Soup',
 ' Coffee',
 ' Veg Fritters',
 ' Vegetarian',
 ' Babycorn Manchurian',
 ' Mango Salad',
 'Lassi',
 'Coffee',
 ' Chaat',
 'Pizza',
 ' Nachos',
 ' Salads',
 ' Peri Peri Fries',
 ' Brownie',
 ' Pasta Alfredo',
 ' Spaghetti',
 'Chicken Grill',
 ' Biryani',
 ' Roti',
 ' Chicken Masala',
 'Chaat',
 ' Pav Bhaji',
 ' Dabeli',
 ' Vada Pav',
 ' Tawa Pulav',
 ' Panipuri',
 ' Malpua',
 'Pizza',
 ' Burgers',
 'Burgers',
 ' Panneer Butter Masala',
 ' Pasta',
 ' Hot Dog',
 ' Fries',
 ' Cheesy Pizza',
 ' Potato Wedges',
 'Sandwiches',
 ' Thick Shakes',
 ' Paan Shake',
 ' Brownie Bomb',
 ' Fries',
 ' Spinach Corn Sandwich',
 ' Paneer Peri Peri',
 'Pizza',
 ' Pasta',
 ' Spinach Salsa',
 ' Garlic Bread',
 ' Lasagne',
 ' Bruschettas',
 ' Chocolate Bomb',
 'Friendly Staff',
 'Biryani',
 'Paratha',
 ' Vada Pav',
 ' Chaat',
 ' Pav Bhaji',
 ' Panipuri',
 ' Masala Poori',
 ' Dhokla',
 'Whole Meat Shawarma',
 ' Fries',
 'Panneer Butter Masala',
 ' Lassi',
 ' Paratha',
 ' Jeera Rice',
 ' Paneer Parantha',
 ' Bhindi Fry',
 ' Veg Kolhapuri',
 'Waffles',
 ' Pasta',
 ' Crispy Chicken',
 ' Honey Chilli Chicken',
 ' Sandwich',
 ' Coffee',
 ' Crepe',
 'Masala Papad',
 ' Paneer Satay',
 ' Vegetable Biryani',
 ' Panneer Butter Masala',
 ' Tomato Soup',
 ' Manchow Soup',
 ' Roti',
 'Shawarma',
 ' Biryani',
 ' Bbq Chicken',
 ' Chicken Grill',
 ' Raita',
 'Jowar Roti',
 ' Buttermilk',
 ' Sabudana Vada',
 ' Thali',
 ' Mirchi Bajji',
 ' Brinjal Curry',
 'Burgers',
 ' Caramel Milkshake',
 ' Iced Coffee',
 ' Iced Tea',
 'Supreme Pizza',
 ' Potato Wedges',
 ' Masala Lemonade',
 ' Tandoori Paneer',
 ' Exotic Garlic Bread',
 'Neer Dosa',
 ' Sea Food',
 ' Ghee Rice',
 ' Marwai Sukka',
 ' Egg Biryani',
 ' Kori Roti',
 ' Thai Fish',
 'White Pasta',
 ' Noodles',
 ' Babycorn Manchurian',
 ' Pizza',
 ' Veg Momos',
 ' Fry Momos',
 ' Tawa Pulav',
 'Masala Dosa',
 ' Sandwiches',
 ' Coffee',
 ' Onion Dosa',
 ' Rava Dosa',
 ' Chaat',
 ' Sandwich',
 'French Fries',
 ' Burgers',
 'Paratha',
 ' Boondi Raita',
 ' Roti',
 ' Kulfi',
 ' Panneer Butter Masala',
 ' Sweet Lassi',
 ' Dal Tadka',
 'Veg Momos',
 ' Brownie Chocolate',
 ' Palak Chaat',
 ' Mutton Biryani',
 ' Dahipuri',
 ' Fish',
 ' Sea Food',
 'Noodles',
 ' Pasta',
 ' Schezwan Momos',
 ' Chicken Momo',
 ' Teriyaki Chicken',
 ' Thukpa',
 ' Fries',
 'Spring Roll',
 ' Manchow Soup',
 ' Babycorn Manchurian',
 ' Mint Cooler',
 ' Roti',
 ' Panneer Butter Masala',
 'Biryani',
 'Pho',
 ' Papaya Salad',
 ' Coffee',
 ' Veg Momos',
 ' Rolls',
 'Waffles',
 ' Pasta',
 ' Coleslaw Sandwich',
 ' Choco Waffle',
 ' Tacos',
 ' Momos',
 ' Cheese Nachos',
 'Pizza',
 ' Pasta',
 ' Salads',
 ' Calamari',
 ' Ravioli',
 ' Pumpkin Soup',
 ' Wine',
 'Nachos',
 ' Cup Cake',
 ' Hot Chocolate Fudge',
 ' Salsa',
 ' Cheesy Garlic Bread',
 ' Waffles',
 ' Tiramisu',
 'Lime Soda',
 ' Manchow Soup',
 ' Biryani',
 ' Lemon Chicken',
 ' Tomato Soup',
 ' Butter Naan',
 ' Chilli Chicken',
 'Murgh Ghee Roast',
 ' Fish Fry',
 ' Ghee Roast Paneer',
 ' Neer Dosa',
 ' Appam',
 ' Sea Food',
 ' Chicken Sukha',
 'Ragi Mudde',
 ' Lemon Chicken',
 ' Chilli Chicken',
 ' Chicken Curry',
 ' Mutton Chops',
 ' Mutton Biryani',
 'Pasta',
 ' Potato Fries',
 ' Potato Wedges',
 ' Hot Chocolate',
 ' Chaat',
 ' Filter Coffee',
 ' Americano',
 'Sweet Lassi',
 ' Gulab Jamun',
 ' Palak Paneer Paratha',
 ' Paneer Parantha',
 'Pizza',
 ' White Sauce Pasta',
 ' Masala Garlic Bread',
 'Burgers',
 ' Fries',
 ' Mojito',
 ' Chicken Burger',
 'Paratha',
 ' Sandwich',
 ' Chilli Idli',
 ' Noodles',
 ' Sandwiches',
 ' Lime Soda',
 'Chicken Biryani',
 ' Mutton Biryani',
 ' Egg Biryani',
 ' Chicken Fry',
 ' Mutton Fry',
 'Chaat',
 ' Brownie',
 ' Onion Rings',
 ' Tomato Soup',
 ' Paneer Manchurian',
 ' Thali',
 ' Faluda',
 'Sandwiches',
 ' Chocolate Sandwich',
 ' Chaat',
 ' Veg Cheese Sandwich',
 ' Chilli Cheese',
 ' Corn Sandwich',
 'Prawn Biryani',
 ' Andhra Meal',
 ' Veg Thali',
 ' Fry Piece Biryani',
 ' Gongura Chicken',
 ' Paratha',
 'Veggie Delight',
 ' Paneer Tikka',
 ' Salad',
 'Faluda',
 ' Brownie',
 ' Sandwich',
 ' Kulfi',
 ' Fruit Salad',
 ' Sweet Lassi',
 ' Chocolate Fudge',
 'Pav Bhaji',
 ' Masala Dosa',
 ' Idli Vada',
 ' Filter Coffee',
 ' Pongal',
 ' Curd Rice',
 ' Kesari Bath',
 'Roti',
 ' Masala Dosa',
 'Onion Rings',
 ' Pasta',
 ' Kadhai Paneer',
 ' Salads',
 ' Salad',
 ' Roti',
 ' Jeera Rice',
 'Masala Papad',
 ' Paneer Satay',
 ' Paneer Hyderabadi',
 ' Butter Naan',
 ' Roti',
 'Stuffed Mushroom',
 ' Chicken Biryani',
 ' Jal-jeera',
 'Filter Coffee',
 ' Tea',
 ' Hot Coffee',
 ' Hatti Samosa',
 'Masala Dosa',
 'Badam Milk',
 ' Potato Twister',
 ' Pav Bhaji',
 ' Panipuri',
 ' Tikki Chaat',
 ' Cheese Cutlet',
 'Pasta',
 ' Iced Tea',
 'Roti',
 ' Butter Kulcha',
 ' Fried Rice',
 ' Tomato Soup',
 ' Curd Rice',
 ' Lunch Buffet',
 ' Paneer Tikka',
 'Chicken Biryani',
 'Chicken Biryani',
 ' Ragi Mudde',
 ' Mutton Chops',
 'Masala Dosa',
 ' Coffee',
 'Cocktails',
 ' Paneer Tikka',
 ' Rasmalai',
 ' Rabri',
 ' Masala Papad',
 ' Vegetable Biryani',
 ' Chicken Curry',
 'Garlic Bread',
 ' Burgers',
 ' Sandwiches',
 ' Pizza',
 ' Hot Chocolate Fudge',
 ' Waffles',
 ' Potato Wedges',
 'Veggie Pizza',
 ' Garlic Bread',
 ' Lava Cake',
 ' Chocolava',
 ' Spicy Paneer',
 'Kharabath',
 ' Crispy Vada',
 ' Filter Coffee',
 ' Idli Vada',
 ' Idli Chutney',
 ' Kesari Bath',
 ' Gulab Jamun',
 'Noodles',
 ' Veg Momos',
 ' Chowmein',
 ' Schezwan Fried Rice',
 ' Clear Soup',
 ' Sweet Corn Soup',
 ' Chicken Wings',
 'Veg Thali',
 ' Matka Kulfi',
 ' Pongal',
 ' Buttermilk',
 ' Vada',
 ' Ragi Mudde',
 ' Curd Rice',
 'Coffee',
 ' Sandwich',
 ' Hot Chocolate',
 ' Ice Tea',
 ' Sandwiches',
 ' Chicken Lasagne',
 ' Pizza',
 'Jowar Roti',
 ' Buttermilk',
 ' Jolada Rotti',
 ' Brinjal Curry',
 ' Coffee',
 ' Pongal',
 ' Vegetable Biryani',
 'Pizza',
 ' Garlic Bread',
 'Sweet Pongal',
 ' Mini Idlis',
 ' Filter Coffee',
 ' Open Masala Dosa',
 ' Pakoda',
 ' Vada',
 ' Raagi Dosa',
 'Paratha',
 ' Coffee',
 ' Halwa',
 ' Lassi',
 'Vegetable Biryani',
 ' Pineapple Rasam',
 ' Cheese Garlic Naan',
 ' Potato Cheese Ball',
 ' Bamboo Chicken Biryani',
 ' Chicken Skewer',
 ' Fish',
 'Malai Kofta',
 ' Sandwich',
 ' Naan',
 'Momos',
 ' Lunch Buffet',
 ' Chocolate Nirvana',
 ' Thai Green Curry',
 ' Paneer Tikka',
 ' Dum Biryani',
 ' Chicken Biryani',
 'Coffee',
 ' Masala Dosa',
 'Sea Food',
 ' Neer Dosa',
 ' Anjal Masala Fry',
 ' Coconut Pudding',
 ' Chicken Biryani',
 ' Roti',
 ' Fish Curry',
 'Tandoori Chicken',
 ' Rolls',
 'Pasta',
 ' Nachos',
 ' Jalapeno Poppers',
 ' Burrito Bowl',
 ' Ferrero Rocher Shake',
 ' Falafal Sandwich',
 ' Banana Waffles',
 'Pizza',
 ' Mocktails',
 ' Coffee',
 ' Nachos',
 ' Salad',
 ' Pasta',
 ' Sandwiches',
 'Aloo Paratha',
 'Rava Dosa',
 'Momos',
 ' Mushroom Bruschettas',
 ' Jalapeno Cheeseballs',
 ' Pasta',
 ' Red Velvet Shake',
 ' Red Velvet Milkshake',
 ' Maggi',
 'Pesto Pizza',
 'Sandwiches',
 ' Noodles',
 ' Sandwich',
 'Lasagne',
 ' Steak',
 ' Masala Quesadilla',
 ' Garlic Bread',
 'Breakfast Buffet',
 ' Kulcha',
 ' Almond Soup',
 ' Lunch Buffet',
 ' Roti',
 ' Mocktails',
 ' Vegetarian',
 'Hara Bhara Kabab',
 ' Crispy Corn',
 ' Patiala Paneer',
 ' Panneer Butter Masala',
 ' Dum Biryani',
 ' Jal-jeera',
 ' Butter Naan',
 'Waffles',
 ' Cup Cake',
 ' Chocolate Cake',
 'Waffles',
 'Momos',
 ' Burgers',
 ' Rolls',
 ' Sandwich',
 ' Hot Chocolate',
 ' Tea',
 ' Naga Thali',
 'Freak Shake',
 ' Fries',
 ' Pasta Alfredo',
 'Butter Chicken',
 ' Chicken Grill',
 ' Tandoori Chicken',
 ' Noodles',
 ' Biryani',
 ' Fried Rice Chicken',
 'Pizza',
 ' Pasta',
 ' Nachos',
 ' Burgers',
 ' Sandwich',
 ' Tea',
 ' Cheese Balls',
 'Masala Chai',
 ' Coffee',
 ' Masala Tea',
 ' Maggi',
 'Pasta',
 ' Gelato',
 ' Garlic Bread',
 ' Mojito',
 ' Nachos',
 ' Paneer Pizza',
 ' Mocktails',
 'Salad',
 ' Mocktails',
 ' Chocolava',
 ' Pesto Pasta',
 ' Momos',
 ' Jasmine Rice',
 ' Tom Yum Soup',
 'Vada',
 ' Masala Dosa',
 ' Idli',
 'Coffee',
 ' Masala Dosa',
 ' Vegetable Biryani',
 'Pasta',
 ' Pizza',
 ' Burgers',
 ' Pancakes',
 ' Garlic Bread',
 'Maggi',
 ' Pasta',
 ' Burgers',
 ' Sandwiches',
 ' Fries',
 'Sea Food',
 ' Neer Dosa',
 'Pizza',
 'Brownie',
 ' Mojito',
 ' Prawn Ghee Roast',
 ' Corn Tikki',
 ' Mushroom Ghee Roast',
 ' Pinacolada',
 ' Biryani',
 'Biryani',
 ' Thick Shakes',
 ' Fries',
 'Vegetarian',
 'Sweet Pongal',
 ' Curd Rice',
 'Pesto Pizza',
 ' Buffalo Wings',
 ' Cocktails',
 ' Beer',
 ' Egg Ghee Roast',
 ' Brownie',
 ' Pepper Chicken',
 'Waffles',
 ' Sandwiches',
 ' Maggi',
 ' Thick Shakes',
 ' Chocolate Sandwich',
 'Honey Chilli Potatoes',
 ' Nachos',
 ' Lemonade',
 ' Hakka Noodles',
 ' Babycorn Manchurian',
 ' Mushroom Manchurian',
 ' Spring Roll',
 'Manchow Soup',
 ' Momos',
 ' Wonton Soup',
 ' Chowmein',
 ' American Chopsuey',
 ' Chop Suey',
 ' Manchurian',
 'Pasta',
 ' Pizza',
 ' Nachos',
 ' Gelato',
 ' Ravioli',
 ' Jalapeno Poppers',
 ' Cocktails',
 'Masala Vada Pav',
 ' Cheese Vada Pav',
 ' Fries',
 'Corn Augratin',
 ' Lasagne',
 ' Fries',
 ' Garlic Bread',
 ' Pili Pili Pizza',
 ' Mexican Rice',
 ' Ravioli',
 'Salads',
 ' Sandwiches',
 ' Pasta',
 ' Mocktails',
 ' Corn Sandwich',
 ' Garden Salad',
 ' Miso Salad',
 'Coffee',
 ' Thali',
 ' Kharabath',
 ' Masala Dosa',
 ' Idli Vada',
 ' Vanilla Ice Cream',
 ' Bisi Bele Bhaath',
 'Veg Jaipuri',
 ' Neer Dosa',
 ' Kulcha',
 ' Pepper Chicken',
 ' Chilli Mushroom',
 ' Murgh Platter',
 ' Paneer Tikka',
 'Pizza',
 ' Pasta Alfredo',
 ' Brownie',
 ' Garlic Bread',
 ' Sandwiches',
 ' Lemon Mojito',
 ' Mocha Fudge',
 'Mocktails',
 ' Tomato Soup',
 ' Pasta',
 ...]

Displaying the indices of dataset

In [41]:
df.index=range(df.shape[0])
display(df.index)

RangeIndex(start=0, stop=23248, step=1)

Finding out the most liked dishes and getting it value counts and displaying the top most 30 liked dishes

In [42]:
print("Count of Most liked dishes")
favourite_food = pd.Series(likes).value_counts()
display(favourite_food.head(30))

Count of Most liked dishes

 Pasta              2692
 Pizza              1915
 Cocktails          1880
 Burgers            1736
 Mocktails          1623
 Biryani            1307
 Sandwiches         1287
Burgers             1256
 Coffee             1184
 Nachos             1170
 Fish               1116
 Paratha            1107
 Salads             1055
 Chicken Biryani    1004
Cocktails            891
 Fries               876
 Noodles             854
 Beer                835
 Mutton Biryani      832
 Tea                 819
Coffee               801
 Sandwich            788
 Butter Chicken      782
 Thali               770
Biryani              749
Pizza                747
 Roti                729
 Brownie             726
 Salad               677
 Hot Chocolate       672
dtype: int64

plotting the bar graph with the Most liked foods and number of likes it got and saving the PNG file

In [43]:
ax = favourite_food.nlargest(n=20, keep='first').plot(kind='bar',figsize=(20,15),title = 'Top 30 Favourite Food counts ')
for i in ax.patches:
    ax.annotate(str(i.get_height()), (i.get_x() * 1.005, i.get_height() * 1.005))
plt.savefig('Top 30 Favourite Food counts.png')

Displaying the value counts of Restaurants type

In [44]:
print (df['rest_type'].value_counts().head(50))

Casual Dining                    7326
Quick Bites                      5250
Cafe                             2375
Dessert Parlor                   1083
Casual Dining, Bar                985
Delivery                          704
Bar                               358
Takeaway, Delivery                342
Bar, Casual Dining                336
Pub                               324
Fine Dining                       311
Casual Dining, Cafe               304
Lounge                            298
Pub, Casual Dining                224
Food Court                        220
Bakery                            166
Cafe, Casual Dining               159
Dessert Parlor, Cafe              143
Beverage Shop, Quick Bites        137
Beverage Shop                     137
Cafe, Bakery                      128
Casual Dining, Pub                124
Microbrewery, Casual Dining       121
Cafe, Dessert Parlor              111
Sweet Shop                        105
Bakery, Quick Bites                96
Sweet Shop, Quick Bites            88
Pub, Microbrewery                  76
Dessert Parlor, Quick Bites        71
Bakery, Cafe                       69
Bakery, Dessert Parlor             62
Quick Bites, Dessert Parlor        55
Mess                               49
Dessert Parlor, Beverage Shop      49
Casual Dining, Microbrewery        47
Kiosk                              44
Beverage Shop, Dessert Parlor      44
Microbrewery, Pub                  42
Quick Bites, Sweet Shop            41
Cafe, Quick Bites                  40
Fine Dining, Bar                   40
Pub, Cafe                          40
Lounge, Casual Dining              37
Club                               37
Lounge, Bar                        36
Quick Bites, Bakery                35
Food Court, Quick Bites            34
Quick Bites, Beverage Shop         31
Pub, Bar                           28
Microbrewery                       28
Name: rest_type, dtype: int64

Plotting the bar graph for the Restaurant Types and saving the PNG file

In [45]:
plt.figure(figsize=(15,12))
rest = df['rest_type'].value_counts()[:20]
sns.barplot(x=rest,y=rest.index)
plt.title('Restaurant Types')
plt.savefig('Restaurants Types.png')
plt.xlabel('count')
plt.show()

Data Preprocessing¶

Label Encoding the online order column from the dataset for fitting the alggorithm

In [46]:
df['online_order']=df['online_order'].astype('category')
df['online_order']=df['online_order'].cat.codes
print(df.online_order.value_counts())

1    16378
0     6870
Name: online_order, dtype: int64

Label Encoding the Book table column from the dataset for fitting the alggorithm

In [47]:
df['book_table']=df['book_table'].astype('category')
df['book_table']=df['book_table'].cat.codes
df.book_table=pd.to_numeric(df.book_table)
df.book_table.value_counts()
Out[47]:
0    17191
1     6057
Name: book_table, dtype: int64

Label Encoding all the required columns from the dataset for fitting the alggorithm

In [48]:
from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df.location = le.fit_transform(df.location)
df.rest_type=le.fit_transform(df.rest_type)
df.cuisines=le.fit_transform(df.cuisines)
df.menu_item=le.fit_transform(df.menu_item)

Getting all the required columns to the new dataframe and exporting it as a comma seperated file(csv).

In [49]:
my_data = df.iloc[:,[2,3,4,5,6,7,9,10,12]]
my_data.to_csv('cleaned_data_zomato.csv',index=False)

Assigning the dependent and independent variables

In [50]:
x = df.iloc[:,[2,3,5,6,7,9,10,12]]
y = df['rate']

Model Fitting¶

Splitting the dependent and independent variable into training and test dataset using train test split.

In [51]:
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=10)

Fitting the Linear Regression Model with the train dependent and train independent variable and getting the ,r2 Score between the predicted values and dependent test dataset

In [52]:
lr = LinearRegression()
lr.fit(x_train,y_train)
y_pred=lr.predict(x_test)
from sklearn.metrics import r2_score
print(r2_score(y_test,y_pred))

0.2281882852296726

Fitting the Random Forest Regressor Model with the train dependent and train independent variable and getting the r2 Score between the predicted values and dependent test dataset

In [53]:
from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor(n_estimators=650,random_state=245,min_samples_leaf=.0001)
rf.fit(x_train,y_train)
y_pred = rf.predict(x_test)
print(r2_score(y_test,y_pred))

0.8809706960047533

Fitting the Extra Trees Regressor Model with the train dependent and train independent variable and getting the r2 Score between the predicted values and dependent test dataset

In [54]:
ET = ExtraTreesRegressor(n_estimators=120)
ET.fit(x_train,y_train)
y_pred=ET.predict(x_test)
print(r2_score(y_test,y_pred))

0.932504537384086

Passing some of list parameter for the Random Forest Regressor to run with RandomizedSearchCV

In [55]:
n_estimators = [100,200,300,400,500,600,700,800]
max_features = ['auto','sqrt']
max_depth = [int(x) for x in np.linspace(5,30,num=6)]
min_samples_split=[2,5,10,15,100]
min_samples_leaf = [1,2,5,10]
min_weight_fraction_leaf = [0.0,0.1,0.2,0.3,0.4,0.5]

Fitting the Random Forest Regressor model with the above mentioned parameter in the RandomizedSearchCV Algorithm

In [56]:
random_grid = {'n_estimators':n_estimators,
               'max_features':max_features,
               'max_depth':max_depth,
               'min_samples_split':min_samples_split,
               'min_samples_leaf':min_samples_leaf,
               'min_weight_fraction_leaf':min_weight_fraction_leaf}

rf = RandomForestRegressor()
rf = RandomizedSearchCV(estimator=rf,param_distributions=random_grid,
                        scoring='r2',n_iter=100,cv=10,verbose=0,random_state=42,n_jobs=1)
rf.fit(x_train,y_train)
Out[56]:
RandomizedSearchCV(cv=10, estimator=RandomForestRegressor(), n_iter=100,
                   n_jobs=1,
                   param_distributions={'max_depth': [5, 10, 15, 20, 25, 30],
                                        'max_features': ['auto', 'sqrt'],
                                        'min_samples_leaf': [1, 2, 5, 10],
                                        'min_samples_split': [2, 5, 10, 15,
                                                              100],
                                        'min_weight_fraction_leaf': [0.0, 0.1,
                                                                     0.2, 0.3,
                                                                     0.4, 0.5],
                                        'n_estimators': [100, 200, 300, 400,
                                                         500, 600, 700, 800]},
                   random_state=42, scoring='r2')

Getting the Best Parameters of the algorithm,r2 score between the predicted values and dependent test dataset

In [57]:
print(rf.best_params_)
y_pred = rf.predict(x_test)
display('\n',r2_score(y_test,y_pred))

{'n_estimators': 700, 'min_weight_fraction_leaf': 0.0, 'min_samples_split': 5, 'min_samples_leaf': 1, 'max_features': 'auto', 'max_depth': 25}

'\n'
0.8969384866998245

create the pickle file and Loading the Pickle file with the algorithm which gives highest accuracy score and predicting the dependent variable with the r2 Score and best parameters

In [58]:
import pickle
pickle.dump(ET,open('ET Pickle File.pkl','wb'))
model = pickle.load(open('ET Pickle File.pkl','rb'))
pred1 = model.predict(x_test)
print (r2_score(pred1,y_test))

0.9262784174224225